In recent years, several approaches to develop computer aided diagnosis (CAD) systems for dementia have been proposed. Some of these systems analyze neurological brain images by means of machine learning ... [more ▼]

In recent years, several approaches to develop computer aided diagnosis (CAD) systems for dementia have been proposed. Some of these systems analyze neurological brain images by means of machine learning algorithms in order to find the patterns that characterize the disorder, and a few combine several imaging modalities to improve the diagnostic accuracy. However, they usually do not use neuropsychological testing data in that analysis. The purpose of this work is to measure the advantages of using not only neuroimages as data source in CAD systems for dementia but also neuropsychological scores. To this aim, we compared the accuracy rates achieved by systems that use neuropsychological scores beside the imaging data in the classification step and systems that use only one of these data sources. In order to address the small sample size problem and facilitate the data combination, a dimensionality reduction step (implemented using three different algorithms) was also applied on the imaging data. After each image is summarized in a reduced set of image features, the data sources were combined and classified using three different data combination approaches and a Support Vector Machine classifier. That way, by testing different dimensionality reduction methods and several data combination approaches, we aim not only highlighting the advantages of using neuropsychological scores in the classification, but also implementing the most accurate computer system for early dementia detention. The accuracy of the CAD systems were estimated using a database with records from 46 subjects, diagnosed with MCI or AD. A peak accuracy rate of 89% was obtained. In all cases the accuracy achieved using both, neuropsychological scores and imaging data, was substantially higher than the one obtained using only the imaging data. [less ▲]

in Social Cognitive and Affective Neuroscience (2014), 9(10), 1458-1463

Anosognosia is a complex symptom corresponding to a lack of awareness of one’s current clinical status. Anosognosia for cognitive deficits has frequently been described in Alzheimer’s disease (AD), while ... [more ▼]

Anosognosia is a complex symptom corresponding to a lack of awareness of one’s current clinical status. Anosognosia for cognitive deficits has frequently been described in Alzheimer’s disease (AD), while unawareness of current characteristics of personality traits has rarely been considered. We used a well-established questionnaire-based method in a group of 37 AD patients and in healthy controls to probe self- and hetero-evaluation of patients’ personality and we calculated differential scores between each participant’s and his/her relative’s judgments. A brain-behavior correlation was performed using FDG-PET images. The behavioral data showed that AD patients presented with anosognosia for current characteristics of their personality and their anosognosia was primarily explained by impaired third perspective taking. The brain-behavior correlation analysis revealed a negative relationship between anosognosia for current characteristics of personality and dorsomedial prefrontal cortex (dMPFC) activity. Behavioral and neuroimaging data are consistent with the view that impairment of different functions subserved by the dMPFC (self-evaluation, inferences regarding complex enduring dispositions of self and others, confrontation of perspectives in interpersonal scripts) plays a role in anosognosia for current characteristics of personality in AD patients. [less ▲]